---
title: "JMU paper dashboard"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
#storyboard: true
social: menu
source_code: embed
theme: readable
---
```{r setup, include=TRUE, echo=FALSE, message=FALSE, warning=FALSE}
library(flexdashboard)
library(here)
library(kableExtra)
library(tidyverse, quietly=T)
library(forcats, quietly=T)
library(cowplot, quietly=T) # publication-ready theme for ggplot2
library(scales, quietly=T)
library(DT, quietly=T)
# library(plotly,quietly=T)
# library(crosstalk)
# library(data.table,quietly=T)
# library(htmltools)
# library(htmlTable,quietly=T)
# Source: http://lenkiefer.com/2017/01/22/a-guide-to-building-an-interactive-flexdashboard/
# Source: https://beta.rstudioconnect.com/jjallaire/htmlwidgets-showcase-storyboard/htmlwidgets-showcase-storyboard.html
```
```{r all_data, echo=FALSE}
load(file = here("data", "all_data.rda"))
n_md <- nlevels(all_data$md_desc)
n_papers = nrow(all_data)
assessment_type <- c("NO ASSESSMENT", "USABILITY/UX", "EFFECT AND USABILITY/UX", "EFFECT")
assessment_type_n <- sapply(assessment_type, FUN = function(x) {nrow(filter(all_data, val_type == x))})
n_assessment_no <- assessment_type_n[[1]]
n_assessment_yes <- n_papers - n_assessment_no
# Keep the same order of mental disorder for all charts
temp_for_order <-
all_data %>%
group_by(md_id, md_desc) %>%
summarise(number_cases = n()) %>%
mutate(proportion = number_cases/n_papers) %>%
arrange(desc(proportion), md_id)
# convert to factor to retain sorted order of mental disorders
temp_for_order$md_desc <- factor(temp_for_order$md_desc, levels=unique(temp_for_order$md_desc))
# Save ordered mental disorders
md_all_ordered <- levels(temp_for_order$md_desc)
# Adjust labels length for figures production, based on the order of MD given in 'md_all_ordered'
md_lbl_production <- c(
"Depressive disorders",
"Various disorders",
"Anxiety disorders",
"Substance-related and\n addictive disorders",
"Schizophrenia spectrum and\n other psychotic disorders",
"Trauma and\n stressor-related disorders",
"Suicidal behavior disorder/\nnonsuicidal self-injury",
"Comorbid disorders",
"Bipolar and related disorders",
"Obsessive-compulsive and\n related disorders",
"Neurodevelopmental disorders",
"Feeding and eating disorders",
"Sleep-wake disorders",
"Personality disorders",
"Major and mild\n neurocognitive disorders")
default_palette <- c("NO ASSESSMENT"="#AF8DC3", "USABILITY/UX"="#D9F0D3", "EFFECT AND USABILITY/UX"="#7FBF7B", "EFFECT"="#1B7837")
```
# About {data-navmenu="Explore"}
## {data-width=200}
### Number of papers {.value-box}
```{r}
valueBox(n_papers, icon = "fa-file-medical-alt", color = "primary")
```
### Years {.value-box}
```{r}
valueBox("2013-2018", icon = "fa-calendar-alt", color = "info")
```
### Mental disorders covered
```{r}
valueBox(n_md, icon = "fa-notes-medical", color = "primary")
```
### Top disorder
```{r}
valueBox("depression", icon = "fa-notes-medical", color = "info")
```
### Top6 disorders
```{r}
valueBox("73%", icon = "fa-notes-medical", color = "primary")
```
##
### Citation {data-height=100}
Ignacio Miralles, Carlos Granell, Laura Díaz-Sanahuja, William Van Woensel, Juana Bretón-López, Adriana Mira, Diana Castilla, Sven Casteleyn (2020). __Smartphone apps for the treatment of mental disorders: a systematic review__. _JMIR mHealth and uHealth_, 8(3): e14897, 2020. [](https://doi.org/10.2196/14897)
Data and Software Availability: [](https://doi.org/10.5281/zenodo.3609267)
### Abstract
__Background__: Smartphone apps are an increasingly popular means for providing psychological
interventions to patients suffering from a mental disorder. In line with this popularity, there is a need to analyze and summarize the state of the art, both from a psychological and technical perspective.
__Objective__: The objective of this study is to systematically review the literature on the use of
smartphones for psychological interventions. Our systematic review has the following objectives: (i)
analyze the coverage of mental disorders in research articles per year; (ii) study the types of
assessment in research articles per mental disorder per year; (iii) map the use of advanced technical
features, such as sensors, and novel software features, such as personalization and social media, per
mental disorder; (iv) provide an overview of smartphone apps per mental disorder; and (v) provide
an overview of the key characteristics of empirical assessments with rigorous designs (i.e.,
Randomized Controlled Trials, RCT).
__Methods__: The PRISMA guidelines for systematic reviews were followed. We performed searches in
Scopus, Web of Science, APA PsycNET and MEDLINE, covering a period of six years (2013–2018).
We included papers that describe the use of smartphone apps to deliver psychological interventions
for known mental disorders. We formed multidisciplinary teams, comprising experts in psychology
and computer science, to select and classify articles based on psychological and technical features.
__Results__: We found 158 articles that met the inclusion criteria. We observed an increasing interest in smartphone-based interventions over time. Most research targets disorders with high prevalence, i.e.,
depressive (19.6%) and anxiety disorders (11.4%). Around 73% of the papers focus on 6 mental
disorders: depression, anxiety, trauma and stressor-related, substance-related and addiction,
schizophrenia spectrum and other psychotic disorders, or a combination of disorders. More than half
of known mental disorders are not or very scarcely (<3%) represented. An increasing number of
studies are dedicated to assessing clinical effects, but RCT are still a minority (15.8%). From a
technical viewpoint, interventions are leveraging the improved modalities (screen, sound) and
interactivity of smartphones, but only sparingly leverage their truly novel capabilities, such as
sensors, alternative delivery paradigms and analytical methods.
__Conclusions__: There is a need for designing interventions for the full breadth of mental disorders,
rather than primarily focusing on most prevalent disorders. We further contend that an increasingly
systematic focus, i.e., involving RCT, is needed to improve the robustness and trustworthiness of
assessments. Regarding technical aspects, we argue that further exploration and innovative use of
their novel capabilities is needed to fully realize the potential of smartphones for the treatment of
mental health disorders.
# Storyboard {.storyboard data-navmenu="Explore"}
### Table 1. Distribution of assessment types
```{r tab1, echo=FALSE}
data_tbl_assess <-
all_data %>%
select(val_type) %>%
group_by(val_type) %>%
summarise(n = n()) %>%
mutate(n_lbl = if_else(val_type == "NO ASSESSMENT",
paste0(n, "/",n_papers, " (", round(n/n_papers*100,1), "%)*"),
paste0(n, "/",n_assessment_yes, " (", round(n/n_assessment_yes*100,1), "%)†")))
data_tbl_rct <-
all_data %>%
select(val_type, val_edrct) %>%
filter(val_edrct == "RCT") %>%
group_by(val_type, val_edrct) %>%
summarise(n = n()) %>%
ungroup() %>%
mutate(n_papers_rct = sum(n),
n_lbl = paste0(n, "/",n_papers_rct," (", round(n/n_papers_rct*100,1), "%)‡"))
n_papers_rct <- data_tbl_rct$n_papers_rct[1]
data_tbl_pilotrct <-
all_data %>%
select(val_type, val_edrct) %>%
filter(val_edrct == "PILOT RCT") %>%
group_by(val_type, val_edrct) %>%
summarise(n = n()) %>%
ungroup() %>%
mutate(n_papers_pilot = sum(n),
n_lbl = paste0(n, "/",n_papers_pilot, " (", round(n/n_papers_pilot*100,1), "%)‡"))
n_papers_pilot <- data_tbl_pilotrct$n_papers_pilot[1]
#### Special case: there is no USABILITY/UX assessment with Pilot RCT. Add case
data_tbl_pilotrct <-
data_tbl_pilotrct %>%
add_row(val_type = assessment_type[2],
val_edrct = "PILOT RCT",
n = 0,
n_papers_pilot = n_papers_pilot,
n_lbl = paste0(n, "/",n_papers_pilot," (", round(n/n_papers_pilot*100,1), "%)‡"))
data_tbl_assesstype <-
data_tbl_assess %>%
full_join(data_tbl_rct, by="val_type", suffix=c("",".rct")) %>%
full_join(data_tbl_pilotrct, by="val_type", suffix=c("",".pilot")) %>%
select(-n, -val_edrct, -n.rct, -val_edrct.pilot, -n.pilot)
data_tbl_assesstype$val_type <- forcats::fct_relevel(data_tbl_assesstype$val_type, assessment_type)
# Aggregate the sum of the theee types of assessment
data_tbl_assesstype <-
data_tbl_assesstype %>%
arrange(val_type) %>%
add_row(.after = 1,
val_type="ASSESSMENT (total)",
n_lbl=paste0(n_assessment_yes, "/",n_papers, " (", round(n_assessment_yes/n_papers*100,1), "%)*"),
n_lbl.rct=paste0(n_papers_rct, "/",n_assessment_yes, " (", round(n_papers_rct/n_assessment_yes*100,1), "%)†"),
n_lbl.pilot=paste0(n_papers_pilot, "/",n_assessment_yes , " (", round(n_papers_pilot/n_assessment_yes*100,1), "%)†"))
# Table output with Kable
options(knitr.kable.NA = 'N/A')
data_tbl_assesstype %>%
select(`Assessment type` = val_type,
`ALL - N (%)` = n_lbl,
`RCT - N (%)` = n_lbl.rct,
`PILOT RCT - N (%)` = n_lbl.pilot) %>%
knitr::kable(format="html", escape = T, booktabs = TRUE,
caption = "Distribution of assessment types.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
row_spec(1, bold = T, color = "white", background = default_palette[["NO ASSESSMENT"]]) %>%
row_spec(3, bold = T, color = "black", background = default_palette[["USABILITY/UX"]]) %>%
row_spec(4, bold = T, color = "black", background = default_palette[["EFFECT AND USABILITY/UX"]]) %>%
row_spec(5, bold = T, color = "white", background = default_palette[["EFFECT"]]) %>%
add_indent(3:5) %>%
column_spec(1, bold = T) %>%
footnote(symbol =
c(paste0("Percentage based on the total number of studies (N=",n_papers,")"),
paste0("Percentage based on the number of studies with an assessment (N=",n_assessment_yes,")"),
paste0("Percentage based on the number of RCT (N=",n_papers_rct,") and Pilot RCT studies (N=",n_papers_pilot,"), respectively")))
```
***
### Figure 1. Flowchart.
```{r echo=FALSE}
path_fig1 <- here("figs", "Figure1.png")
```
### Figure 2. Temporal trend of amount of articles published and assessment type.
```{r fig2_tempdist, fig.width=10, fig.height=22, fig.asp=1}
cols <- c("md", "md_id", "md_desc", "val_type", "year")
data_plot_tempdist <-
all_data %>%
select(cols)
data_plot_tempdist$val_type <- forcats::fct_relevel(data_plot_tempdist$val_type, assessment_type)
data_plot_tempdist <-
data_plot_tempdist %>%
group_by(year, val_type) %>% # first create counts for each group
summarise(number_cases = n()) %>%
mutate(total_cases_per_year = sum(number_cases),
proportion = number_cases/total_cases_per_year,
proportion_lbl = paste0(round(proportion*100,1), "%"))
# Not run
# display.brewer.all()
# display.brewer.pal(n = 6, name = "PRGn")
# brewer.pal(n = 6, name = "PRGn")[2:5]
pal <- default_palette
plot_tempdist_stakedbarchart <-
data_plot_tempdist %>%
ggplot(aes(x=year, y=number_cases, fill=val_type)) +
geom_bar(stat="identity") +
geom_text(aes(label=paste0(number_cases, " - ", proportion_lbl)), size=2.7, position=position_stack(vjust = 0.5)) +
stat_summary(fun.y = sum, aes(label=..y.., group=year), geom = "text", vjust=-.2) +
labs(title="Temporal trend of articles coloured by assessment type",
x="Year",
y="Number of papers - Percentage of papers [%] per year") +
scale_fill_manual(values = pal) +
scale_x_continuous(breaks = seq(2013,2018, by=1)) +
scale_y_continuous(breaks = seq(0, 60, by=10)) +
guides(fill=guide_legend(title="Assessment type", nrow=4)) + # modify legend title
theme_minimal() +
# Add legend in the top-left corner of the plot
theme(legend.title = element_text(size=11),
# legend.justification = c('right', 'top'),
legend.position = c(0.20, 0.75),
legend.background = element_rect(color = "darkgray", size = 0.5, linetype ="solid"),
legend.key = element_blank()) +
# Change the line type and color of axis lines
theme(axis.line = element_line(colour = "darkgray", size = 0.5, linetype = "solid")) +
theme(panel.grid.minor = element_blank()) +
theme(panel.background = element_blank()) +
theme(plot.margin=unit(rep(20, 4), "pt"))
plot_tempdist_stakedbarchart
```
***
- It shows the temporal distribution of research over the study period 2013-2018, along with their reported assessment type. Overall, we observed a positive evolution of the amount of research over time, steadily increasing from only few (7) articles in 2013 to a much larger amount (60) in 2018.
- Looking at the distribution of assessment types over time, we observe an overall slow proportional increase of studies with an assessment.
- Regarding the type of assessment, we observe that only a fifth of the articles with assessment (22/113, 19.5%) focus specifically on the effect of intervention on clinical symptomatology (drak green).
### Figure 3. Distribution of articles per mental disorder, categorized according to assessment type.
```{r fig3_barchart, echo=FALSE, fig.width=10,fig.asp=0.65}
cols <- c("md", "md_id", "md_desc", "val_type")
data_plot_barchart <-
all_data %>%
select(cols)
data_plot_barchart <-
data_plot_barchart %>%
group_by(md_id, md_desc, val_type) %>% # first create counts for each group
summarise(number_cases = n()) %>%
mutate(proportion = number_cases/n_papers) %>%
ungroup() %>%
group_by(md_id) %>%
mutate(total_cases = sum(number_cases),
proportion_sum = sum(proportion),
proportion_lbl = paste0(round(proportion_sum*100,1), "%")) %>%
ungroup() %>%
mutate(lbl = paste0(round(number_cases/total_cases*100,1), "%")) %>%
arrange(desc(proportion_sum), md_id)
# To keep "unified" order in plot.
data_plot_barchart$md_desc <- forcats::fct_relevel(data_plot_barchart$md_desc, md_all_ordered)
data_plot_barchart$val_type <- forcats::fct_relevel(data_plot_barchart$val_type, assessment_type)
pal <- default_palette
lbls <- distinct(data_plot_barchart, md_desc, proportion_sum, proportion_lbl)
top_proportion <- sum(lbls[1:6, "proportion_sum"])
top_lbl <- paste0(round(top_proportion*100,1), "%")
# Compute aggregated results for assessment types
data_table <-
data_plot_barchart %>%
group_by(val_type) %>%
summarise(total_asstype = sum(number_cases)) %>%
mutate(lbl = paste0(round(total_asstype/n_papers*100,1), "%"))
data_table[1] <- c("No assessment", "Usability/UX", "Effect + Usability/UX", "Effect")
lbl_subtitle <- "Aggregated results of assessment types: "
for (i in 1:nrow(data_table)) {
lbl_subtitle <- paste0(lbl_subtitle, data_table[i,1], " ", data_table[i,2], "/", n_papers,
" (" ,data_table[i,3], ")")
if (i==nrow(data_table))
lbl_subtitle <- paste0(lbl_subtitle, ".")
else
lbl_subtitle <- paste0(lbl_subtitle, ", ")
}
plot_barchart <-
data_plot_barchart %>%
ggplot(aes(x=md_desc, y=proportion, fill=val_type)) +
geom_bar(stat="identity") +
labs(title="Distribution per mental disorder and assessment type",
x="Mental disorders",
y="Number of papers and total percentage",
caption="Source: authors") +
geom_point(aes(y=proportion_sum), size=6, color="white", show.legend = F) +
geom_text(aes(label=number_cases), size=2.5, position=position_stack(vjust = 0.4)) +
# Percentatge inside point
annotate("text", x = lbls$md_desc, y = lbls$proportion_sum,
label = lbls$proportion_lbl, color = "black", size=2, hjust = 0.4, vjust = 0.2) +
# Arrow to indidate Top6 mental disorders
annotate("text", x = "Schizophrenia spectrum and other psychotic disorders", y = .16,
label = top_lbl, color = "black", size = 3, hjust = -0.1, vjust = 1.2) +
geom_segment(aes(x = "Schizophrenia spectrum and other psychotic disorders",
xend = "Substance-related and addictive disorders",
y = .18,
yend = .18),
arrow = arrow(length = unit(0.5,"cm")), color = "black") +
geom_segment(aes(x="Schizophrenia spectrum and other psychotic disorders",
y=0.11,
xend="Schizophrenia spectrum and other psychotic disorders",
yend=0.18), color="black") +
coord_flip() +
scale_fill_manual(name="Assessment type", values = pal) +
scale_x_discrete(labels = md_lbl_production) +
scale_y_continuous(expand=c(0,0), labels=scales::percent_format(accuracy=1), breaks=seq(0, 0.21, by=0.02), limits=c(0, 0.21)) +
# Which legend to show
guides(fill=guide_legend(title="Assessment type", nrow=4)) + # modify legend title
theme_minimal() +
theme(legend.title = element_text(size=11),
legend.position = c(0.70, 0.75),
legend.background = element_rect(color = "darkgray", size = 0.5, linetype ="solid"),
legend.key = element_blank()) +
# Change the line type and color of axis lines
theme(axis.line = element_line(colour = "darkgray", size = 0.5, linetype = "solid")) +
theme(panel.grid.major = element_blank()) +
theme(panel.grid.minor = element_blank()) +
theme(panel.background = element_blank()) +
theme(plot.margin=unit(rep(20, 4), "pt"))
plot_barchart
```
***
- It shows the number of studies per mental disorder, ranked in ascending order and subcategorized according to the type of assessment.
- `r lbl_subtitle`
### Figure 4. Distribution of articles published for the top 6 mental disorders over time.
```{r fig4_linechart, echo=FALSE, fig.width=12, fig.height=14, fig.asp=0.65}
n_top <- 6
md_top <- md_all_ordered[1:n_top]
md_top_lbls <- md_lbl_production[1:n_top]
data_plot_topmd <-
all_data %>%
filter(md_desc %in% md_top) %>%
group_by(md_desc, year) %>%
summarise(number_cases = n()) %>%
ungroup() %>%
mutate(md_desc = factor(md_desc)) %>%
arrange(desc(number_cases))
#' Add mental disorders with zero cases
for (y in 2013:2018) {
md_year <- filter(data_plot_topmd, year==y) %>% select(md_desc)
md_dif <- setdiff(md_top, md_year$md_desc)
if (length(md_dif) > 0) {
for (md in md_dif) {
data_plot_topmd <- add_row(data_plot_topmd, md_desc=md, year=y, number_cases=0)
}
}
}
data_plot_topmd$md_desc <- forcats::fct_relevel(data_plot_topmd$md_desc, md_top)
brks <- levels(data_plot_topmd$md_desc)
plot_topmd <-
data_plot_topmd %>%
ggplot(aes(x=year, y=number_cases, group=md_desc)) +
geom_line(aes(color=md_desc), size=2.5, alpha=.4) +
geom_point(shape=21, size=9, color="darkgray", fill="white") +
labs(title="Distribution top mental disorders per year",
subtitle = paste0("Top mental disorders (", top_lbl,")"),
x="Year",
y="Number of papers",
caption="Source: authors") +
scale_color_brewer(name="Mental disorders", palette="Set2", breaks=brks) +
geom_text(aes(label = number_cases), color= "black", size=3) +
scale_y_continuous(breaks=seq(0,13,by=1)) +
theme_minimal() +
# Legend: Top-Left Inside the Plot"
theme(legend.title = element_text(size=11),
legend.justification = c('left', 'top'),
legend.position=c(0.05, 0.95),
legend.background = element_rect(color = "darkgray", size = 0.5, linetype ="solid"),
legend.key = element_blank()) +
theme(panel.grid.major = element_blank()) +
theme(panel.grid.minor = element_blank()) +
theme(panel.background = element_blank()) +
theme(plot.margin=unit(rep(20, 4), "pt")) +
# Change the line type and color of axis lines
theme(axis.line = element_line(colour = "darkgray", size = 0.5, linetype = "solid"))
plot_topmd
```
***
- It shows the temporal trend of the top six mental disorders targeted by studies over the period 2013-2018.
- Overall, we observe an increasing number of published articles related to the top six mental disorders over time, with a significant increase since 2015.
- We note that the relative ranking of the top six mental disorders is largely maintained since 2015, with two notable exceptions: __trauma and stressor-related disorders__ sharply decreased in 2018, and __various disorders__ significantly increased in the last 2 years, reaching the first and second positions, respectively.
- We also point out the doubling of research on __depressive disorders__, which was already well researched previously, in 2018.
### Table 2. Mental disorders and the studies targeting them.
```{r tab2, echo=FALSE}
unite_paper_ids <- function(mentaldisorder, appname) {
if (!is.na(appname)) {
all_data %>%
filter(md_desc == mentaldisorder & app_name==appname) %>%
arrange(year) %>%
select(id) %>%
as_vector() %>%
stringr::str_c(collapse = ";")
} else {
all_data %>%
filter(md_desc == mentaldisorder & is.na(app_name)) %>%
arrange(year) %>%
select(id) %>%
as_vector() %>%
stringr::str_c(collapse = ";")
}
}
data_kp_apps <-
all_data %>%
group_by(md_desc, app_name) %>%
summarise(number_apps = n()) %>%
arrange(number_apps, md_desc)
data_kp_apps <-
data_kp_apps %>%
add_column(ids = purrr::map2(data_kp_apps$md_desc, data_kp_apps$app_name, unite_paper_ids))
data_kp_apps <-
data_kp_apps %>%
add_column(app_ids = paste0(data_kp_apps$app_name, " (", data_kp_apps$ids, ")"))
data_kp_apps <-
data_kp_apps %>%
group_by(md_desc) %>%
summarise(app_ids_merge = paste0(app_ids, collapse = ", "))
data_kp_apps$md_desc <- forcats::fct_relevel(data_kp_apps$md_desc, md_all_ordered)
# Table output with DT
data_kp_apps %>%
arrange(desc(md_desc)) %>%
select(`Mental Disorder` = md_desc,
`References by app` = app_ids_merge) %>%
datatable(rownames = FALSE,
filter = "top",
class = "table-bordered table-condensed hover",
extensions = "Buttons",
options = list(
pageLength = 7, #autoWidth = TRUE,
dom = 'Bfrtip', # https://datatables.net/reference/option/dom
buttons = c('copy', 'csv', 'excel', 'pdf', 'print')
)) %>%
formatStyle('Mental Disorder', fontWeight = 'bold')
```
***
- A tabular, compact distribution of papers (apps) per mental disorder, grouping the references per app. The number(s) in brackets next to the app name is the reference(s) in which the app is mentioned. __NOTE: these references are internal identifiers instead.__
- NA = app name not available/not mentioned.
### Figure 5. Bubble plot representing technology-related dimensions versus mental disorders.
```{r fig5_feat, echo=FALSE, warning=FALSE}
cols = c("id", "md_id", "md_desc",
"feat_use", "feat_promp", "feat_soc", "feat_hcp", "feat_learn",
"feat_prog", "feat_ca", "feat_ass", "feat_vr", "feat_ar", "feat_pers", "feat_game",
"app_name", "year")
cols_feat = c("feat_use", "feat_promp", "feat_soc", "feat_hcp", "feat_learn",
"feat_prog", "feat_ca", "feat_ass", "feat_vr", "feat_ar", "feat_pers", "feat_game")
data_kp_feat <-
all_data %>%
select(cols) %>%
gather(cols_feat, key="tech_type",value="tech_value") %>%
mutate(cat = "Software features") %>%
filter(tech_value == "YES") %>%
group_by(md_desc, tech_type, cat) %>%
summarise(number_cases = n()) %>%
ungroup() # required to add_row()
# For totals in final bubble plot
data_kp_feat_n <-
data_kp_feat %>%
group_by(tech_type) %>%
summarise(n = sum(number_cases)) %>%
arrange(desc(n))
md_feat <- unique(data_kp_feat$md_desc)
md_dif <- setdiff(md_all_ordered, md_feat)
if (length(md_feat) > 0) {
for (md in md_dif) {
# Added "feat_use" (or any value) to avoid NA in the "tech_type" variable. Nothing is drawn in the plot
data_kp_feat <- add_row(data_kp_feat, md_desc=md, tech_type="feat_use", cat="Software features")
}
}
data_kp_feat$md_desc <- forcats::fct_relevel(data_kp_feat$md_desc, md_all_ordered)
```
```{r fig5_sens, echo=FALSE, warning=FALSE}
cols = c("id", "md_id", "md_desc",
"sens_acc", "sens_gyr", "sens_gps", "sens_mic", "sens_cam",
"app_name", "year")
cols_sens <- c("sens_acc", "sens_gyr", "sens_gps", "sens_mic", "sens_cam")
data_kp_sens <-
all_data %>%
select(cols) %>%
gather(cols_sens, key="tech_type", value="tech_value") %>%
mutate(cat = "Built-In sensors") %>%
filter(tech_value == "YES") %>%
group_by(md_desc, tech_type, cat) %>%
summarise(number_cases = n()) %>%
ungroup() # required to add_row()
# For totals in final bubble plot
data_kp_sens_n <-
data_kp_sens %>%
group_by(tech_type) %>%
summarise(n = sum(number_cases)) %>%
arrange(desc(n))
md_sens <- unique(data_kp_sens$md_desc)
md_dif <- setdiff(md_all_ordered, md_sens)
if (length(md_dif) > 0) {
for (md in md_dif) {
# Added "sens_mic" (or any other values) to avoid NA in the "tech_type" variable. Nothing is drawn in the plot
data_kp_sens <- add_row(data_kp_sens, md_desc=md, tech_type="sens_mic", cat="Built-In sensors")
}
}
data_kp_sens$md_desc <- forcats::fct_relevel(data_kp_sens$md_desc, levels=md_all_ordered)
```
```{r fig5_anal, echo=FALSE, warning=FALSE}
cols = c("id", "md", "md_id", "md_desc",
"anal_ml", "anal_beh", "anal_act", "anal_sp",
"app_name", "year")
cols_anal = c("anal_ml", "anal_beh", "anal_act", "anal_sp")
data_kp_anal <-
all_data %>%
select(cols) %>%
gather(cols_anal, key="tech_type",value="tech_value") %>%
mutate(cat = "Analytics") %>%
filter(tech_value == "YES") %>%
group_by(md_desc, tech_type, cat) %>%
summarise(number_cases = n()) %>%
ungroup() # required to add_row()
# For totals in final bubble plot
data_kp_anal_n <-
data_kp_anal %>%
group_by(tech_type) %>%
summarise(n = sum(number_cases)) %>%
arrange(desc(n))
md_anal <- unique(data_kp_anal$md_desc)
md_dif <- setdiff(md_all_ordered, md_anal)
if (length(md_anal) > 0) {
for (md in md_dif) {
# Added "anal_ml" (or any value) to avoid NA in the "tech_type" variable. Nothing is drawn in the plot
data_kp_anal <- add_row(data_kp_anal, md_desc=md, tech_type="anal_ml", cat="Analytics")
}
}
data_kp_anal$md_desc <- forcats::fct_relevel(data_kp_anal$md_desc, levels=md_all_ordered)
```
```{r fig5_alltogether, echo=FALSE, warning=FALSE, fig.width=15, fig.height=14, fig.asp=0.65}
data_kp_all <-
bind_rows(data_kp_feat, data_kp_sens, data_kp_anal)
data_kp_all$tech_type <- as_factor(data_kp_all$tech_type)
# Replace existing factors levels for figure production
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_learn"] <- "Learning"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_prog"] <- "Progress"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_pers"] <- "Personalization"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_ass"] <- "Assessment"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_promp"] <- "Prompting"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_hcp"] <- "Health Care Provider \nCommunication"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_use"] <- "In-Situ Use"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_soc"] <- "Social"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_ca"] <- "Context-Awareness"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_game"] <- "Game"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_vr"] <- "Virtual Reality"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_ar"] <- "Augmented Reality"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="sens_mic"] <- "Microphone"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="sens_gps"] <- "GPS"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="sens_cam"] <- "Camera"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="sens_acc"] <- "Accelerometer"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="sens_gyr"] <- "Gyroscope"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="anal_ml"] <- "Machine Learning"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="anal_act"] <- "Activity Analysis"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="anal_beh"] <- "Behavioral Analysis"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="anal_sp"] <- "Spatial Analysis"
cols_ordered <- c("Learning", "Progress", "Personalization", "Assessment", "Prompting", "Health Care Provider \nCommunication",
"In-Situ Use", "Social", "Context-Awareness", "Game", "Virtual Reality", "Augmented Reality",
"Microphone", "GPS", "Camera", "Accelerometer", "Gyroscope",
"Machine Learning", "Activity Analysis", "Behavioral Analysis", "Spatial Analysis")
dims_ordered <- c("Software features", "Built-In sensors", "Analytics")
data_kp_all$tech_type <- forcats::fct_relevel(data_kp_all$tech_type, cols_ordered)
data_kp_all_n <-
bind_rows(data_kp_feat_n, data_kp_sens_n, data_kp_anal_n)
data_kp_all_n <-
data_kp_all_n %>%
mutate(tech_label = cols_ordered)
data_kp_all$cat <- factor(data_kp_all$cat)
data_kp_all$cat <- forcats::fct_relevel(data_kp_all$cat, dims_ordered)
brks <- levels(factor(data_kp_all$cat))
# Color per dimension:
# Software features "#FC8D62"; Built-In sensors "#66C2A5"; Types of analyses "#8DA0CB"
lbls_colors <-
ifelse(unique(data_kp_all$tech_type) %in% c("Learning", "Progress", "Personalization", "Assessment", "Prompting", "Health Care Provider \nCommunication", "In-Situ Use", "Social", "Context-Awareness", "Game", "Virtual Reality", "Augmented Reality"), "#FC8D62",
ifelse(unique(data_kp_all$tech_type) %in% c("Microphone", "GPS", "Camera", "Accelerometer", "Gyroscope"), "#66C2A5",
"#8DA0CB"))
kp_bubblechart <-
data_kp_all %>%
ggplot(aes(x=md_desc, y=tech_type, colour=cat)) +
geom_point(aes(size=number_cases), alpha=1, na.rm = TRUE)+#, show.legend = FALSE) +
geom_text(aes(label=number_cases), colour="black", size=3, na.rm = TRUE) +
scale_size_area(max_size=18) +
coord_flip() +
labs(#title="Mental disorders vs Technology",
#subtitle = "Technology-related characteristics are ranked in each dimension",
x="Mental disorders",
y="Technology-related characteristics",
caption="Source: authors") +
scale_color_manual(name="Dimensions", breaks=brks,
values = c("Software features"="#FC8D62","Built-In sensors"="#66C2A5", "Analytics"="#8DA0CB")) +
# Which legend to show
guides(colour="legend",size = "none") +
theme_minimal() +
theme(axis.text.x=element_text(angle=60, size=11, hjust=1, color=lbls_colors),
axis.text.y=element_text(size=11)) +
# Legend: Top-Right Inside the Plot")
theme(legend.title = element_text(size=9),
legend.justification = c('right', 'top'),
legend.position=c(1, 0.90),
legend.background = element_rect(color = "darkgray", size = 0.5, linetype ="solid"),
legend.key = element_blank()) +
# Change the line type and color of axis lines
theme(axis.line = element_line(colour = "darkgray", size = 0.5, linetype = "solid")) +
theme(panel.grid.major.x = element_blank()) +
theme(panel.grid.minor = element_blank()) +
theme(panel.background = element_blank()) +
theme(plot.margin=unit(rep(20, 4), "pt"))
# Add annotations: total of columns
kp_bubblechart <-
kp_bubblechart +
annotate("rect", xmin = 14.40, xmax = 14.75, ymin = 0.5, ymax = 12.4,
fill = "#FC8D62", alpha = 0.6) +
annotate("rect", xmin = 14.40, xmax = 14.75, ymin = 12.6, ymax = 17.4,
fill = "#66C2A5", alpha = 0.6) +
annotate("rect", xmin = 14.40, xmax = 14.75, ymin = 17.6, ymax = 21.5,
fill = "#8DA0CB", alpha = 0.6)
for (i in 1:nrow(data_kp_all_n)) {
kp_bubblechart <-
kp_bubblechart +
annotate("text", x = "Personality disorders", y = data_kp_all_n$tech_label[i], label = data_kp_all_n$n[i],
color = "white", fontface="bold", size=3, hjust = 0.4, vjust = -3)
}
# Add rectangles to highlight group of bubbles
kp_bubblechart <-
kp_bubblechart +
annotate("rect", xmin = 0.5, xmax = 11.5, ymin = 0.5, ymax = 7.5,
fill = "lightgray", alpha = 0.2, linetype="dashed") +
annotate("rect", xmin = 0.5, xmax = 5.5, ymin = 0.5, ymax = 9.5,
fill = "lightgray", alpha = 0.2)
kp_bubblechart
```
***
- It plots the technology-related dimensions, namely, software features implemented by the studied apps (in orange), the utilized built-in sensors (in green), and analytics (in blue), vs the type of mental disorders. Bubble size corresponds with number of articles.
- Within each technology-related dimension (X axis), features are ranked by their decreasing popularity over all mental disorders (left-right; occurrence count is shown at the top of each column); mental disorders (Y axis) are similarly ordered by decreasing popularity in literature (bottom-up).
- It shows to which extent, and for which disorder(s), the state of the art is leveraging hardware- and/or software-related smartphone capabilities.
- We note that larger bubbles tend to be concentrated at the bottom of the graph, as the most popular mental disorders have a higher number of articles, which also tend to cover more varied technical dimensions.
# Data {.data data-navmenu="Explore"}
### All papers
```{r all_papers, echo=FALSE}
all_data %>%
select(`YEAR` = year,
`REF`= id,
`PAPER TITLE` = title,
`MENTAL DISORDER` = md_desc,
`APP NAME` = app_name) %>%
arrange(desc(YEAR)) %>%
datatable(rownames = FALSE,
filter = "top",
class = "table-bordered table-condensed hover",
extensions = "Buttons",
options = list(
pageLength = 8, #autoWidth = TRUE,
dom = 'Bfrtip', # https://datatables.net/reference/option/dom
buttons = c('copy', 'csv', 'excel', 'pdf', 'print')
))
# formatStyle('CBT',
# backgroundColor = styleEqual(c("YES", "NO"), c('red', ''))) %>%
# formatStyle('WAV',
# backgroundColor = styleEqual(c("YES", "NO"), c('orange', ''))) %>%
# formatStyle('TRA',
# backgroundColor = styleEqual(c("YES", "NO"), c('yellow', ''))) %>%
# formatStyle('POS',
# backgroundColor = styleEqual(c("YES", "NO"), c('blue', ''))) %>%
# # formatStyle('GUI',
# # backgroundColor = styleEqual(c("YES", "NO"), c('lightgreen', ''))) %>%
# formatStyle('GES',
# backgroundColor = styleEqual(c("YES", "NO"), c('lightgreen', ''))) %>%
# formatStyle('VOI',
# backgroundColor = styleEqual(c("YES", "NO"), c('lightgreen', ''))) %>%
# formatStyle('TXT',
# backgroundColor = styleEqual(c("YES", "NO"), c('lightgreen', '')))
```
